{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第1步：数据探索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CUST_ID</th>\n",
       "      <th>GENDER</th>\n",
       "      <th>AGE</th>\n",
       "      <th>TENURE</th>\n",
       "      <th>CHANNEL</th>\n",
       "      <th>AUTOPAY</th>\n",
       "      <th>ARPB_3M</th>\n",
       "      <th>CALL_PARTY_CNT</th>\n",
       "      <th>DAY_MOU</th>\n",
       "      <th>AFTERNOON_MOU</th>\n",
       "      <th>NIGHT_MOU</th>\n",
       "      <th>AVG_CALL_LENGTH</th>\n",
       "      <th>BROADBAND</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>34</td>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>203</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.04</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>62</td>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>360</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1910.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.30</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "      <td>55</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>304</td>\n",
       "      <td>0</td>\n",
       "      <td>437.2</td>\n",
       "      <td>200.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.92</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>66</td>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "      <td>55</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>304</td>\n",
       "      <td>0</td>\n",
       "      <td>437.2</td>\n",
       "      <td>182.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.92</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "      <td>55</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>304</td>\n",
       "      <td>0</td>\n",
       "      <td>437.2</td>\n",
       "      <td>214.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.92</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CUST_ID  GENDER  AGE  TENURE  CHANNEL  AUTOPAY  ARPB_3M  CALL_PARTY_CNT  \\\n",
       "0       63       1   34      27        2        0      203               0   \n",
       "1       64       0   62      58        1        0      360               0   \n",
       "2       65       1   39      55        3        0      304               0   \n",
       "3       66       1   39      55        3        0      304               0   \n",
       "4       67       1   39      55        3        0      304               0   \n",
       "\n",
       "   DAY_MOU  AFTERNOON_MOU  NIGHT_MOU  AVG_CALL_LENGTH  BROADBAND  \n",
       "0      0.0            0.0        0.0             3.04          1  \n",
       "1      0.0         1910.0        0.0             3.30          1  \n",
       "2    437.2          200.3        0.0             4.92          0  \n",
       "3    437.2          182.8        0.0             4.92          0  \n",
       "4    437.2          214.5        0.0             4.92          0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1: 加载数据文件，查看数据信息\n",
    "df = pd.read_csv('broadband.csv')\n",
    "df.head() # broadband 即可：0-离开，1-留存"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1114 entries, 0 to 1113\n",
      "Data columns (total 13 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   CUST_ID          1114 non-null   int64  \n",
      " 1   GENDER           1114 non-null   int64  \n",
      " 2   AGE              1114 non-null   int64  \n",
      " 3   TENURE           1114 non-null   int64  \n",
      " 4   CHANNEL          1114 non-null   int64  \n",
      " 5   AUTOPAY          1114 non-null   int64  \n",
      " 6   ARPB_3M          1114 non-null   int64  \n",
      " 7   CALL_PARTY_CNT   1114 non-null   int64  \n",
      " 8   DAY_MOU          1114 non-null   float64\n",
      " 9   AFTERNOON_MOU    1114 non-null   float64\n",
      " 10  NIGHT_MOU        1114 non-null   float64\n",
      " 11  AVG_CALL_LENGTH  1114 non-null   float64\n",
      " 12  BROADBAND        1114 non-null   int64  \n",
      "dtypes: float64(4), int64(9)\n",
      "memory usage: 113.3 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cust_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>tenure</th>\n",
       "      <th>channel</th>\n",
       "      <th>autopay</th>\n",
       "      <th>arpb_3m</th>\n",
       "      <th>call_party_cnt</th>\n",
       "      <th>day_mou</th>\n",
       "      <th>afternoon_mou</th>\n",
       "      <th>night_mou</th>\n",
       "      <th>avg_call_length</th>\n",
       "      <th>broadband</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>135</td>\n",
       "      <td>12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.95</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    cust_id  gender  age  tenure  channel  autopay  arpb_3m  call_party_cnt  \\\n",
       "99       28       0   32      18        2        1      135              12   \n",
       "\n",
       "    day_mou  afternoon_mou  night_mou  avg_call_length  broadband  \n",
       "99      0.0            0.0        0.0             3.95          0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2: 列名全部换成小写\n",
    "df.rename(str.lower, axis='columns', inplace=True)\n",
    "\n",
    "df.sample() # 查看一个样本数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Broadband:  Counter({0: 908, 1: 206})\n"
     ]
    }
   ],
   "source": [
    "# 3: 查看因变量 broadband 分布情况，看是否存在不平衡\n",
    "from collections import Counter\n",
    "\n",
    "print('Broadband: ', Counter(df['broadband'])) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第2步：拆分测试集与训练集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['broadband'] # 标签\n",
    "\n",
    "X = df.iloc[:, 1:-1] # 客户 id 没有用，故丢弃 cust_id\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, \n",
    "                                    test_size=0.4, random_state=123)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第3步：决策树建模"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn.tree as tree\n",
    "\n",
    "# 1. 直接使用交叉网格搜索来优化决策树模型，边训练边优化\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# 2. 网格搜索参数，选择最优参数\n",
    "param_grid = {'criterion': ['entropy', 'gini'], # 树的深度评估指标\n",
    "              'max_depth': [2, 3, 4, 5, 6, 7, 8], # 树的深度\n",
    "              'min_samples_split': [4, 8, 12, 16, 20, 24, 28]} # 最小拆分的叶子样本数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3. 定义一棵树对象\n",
    "clf = tree.DecisionTreeClassifier()  \n",
    "\n",
    "# 4. 传入模型，网格搜索的参数，评估指标，cv交叉验证的次数\n",
    "clfcv = GridSearchCV(estimator=clf, param_grid=param_grid, scoring='roc_auc',cv=4) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=4, estimator=DecisionTreeClassifier(),\n",
       "             param_grid={'criterion': ['entropy', 'gini'],\n",
       "                         'max_depth': [2, 3, 4, 5, 6, 7, 8],\n",
       "                         'min_samples_split': [4, 8, 12, 16, 20, 24, 28]},\n",
       "             scoring='roc_auc')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 5. 训练模型\n",
    "clfcv.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 6. 使用模型来对测试集进行预测\n",
    "test_result = clfcv.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "决策树准确度:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.98      0.93       360\n",
      "           1       0.83      0.44      0.58        86\n",
      "\n",
      "    accuracy                           0.87       446\n",
      "   macro avg       0.85      0.71      0.75       446\n",
      "weighted avg       0.87      0.87      0.86       446\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 7. 模型评估\n",
    "import sklearn.metrics as metrics\n",
    "\n",
    "print(\"决策树准确度:\")\n",
    "print(metrics.classification_report(y_test,test_result))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "决策树 AUC:\n",
      "AUC = 0.7098\n"
     ]
    }
   ],
   "source": [
    "# 8. 决策树的AUC\n",
    "\n",
    "print(\"决策树 AUC:\")\n",
    "fpr_test, tpr_test, th_test = metrics.roc_curve(y_test, test_result)\n",
    "print('AUC = %.4f' %metrics.auc(fpr_test, tpr_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'criterion': 'entropy', 'max_depth': 5, 'min_samples_split': 4}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 9. 网格搜索后的最优参数\n",
    "\n",
    "clfcv.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 10. 将最优参数代入到模型中，重新训练、预测\n",
    "\n",
    "clf2 = tree.DecisionTreeClassifier(criterion='entropy', max_depth=5, min_samples_split=4)\n",
    "\n",
    "clf2.fit(X_train, y_train)\n",
    "\n",
    "test_res2 = clf2.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'user.pdf'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 11. 绘制图形 pip3 install graphviz\n",
    "\n",
    "import graphviz\n",
    "\n",
    "dot_data = tree.export_graphviz(clf2, out_file=None)\n",
    "\n",
    "graph = graphviz.Source(dot_data)\n",
    "\n",
    "graph.render('user')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1094x870 at 0x120343470>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from PIL import Image\n",
    "\n",
    "Image.open('user.jpg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第4步：随机森林建模"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. 网格搜索\n",
    "\n",
    "param_grid = {\n",
    "    'criterion':['entropy','gini'],# 衡量标准\n",
    "    'max_depth':[5, 6, 7, 8],    # 每棵决策树的深度\n",
    "    'n_estimators':[11,13,15],  # 决策树个数 - 随机森林特有参数\n",
    "    'max_features':[0.3,0.4,0.5], # 每棵决策树使用的变量占比 - 随机森林特有参数\n",
    "    'min_samples_split':[4,8,12,16]  # 叶子的最小拆分样本量\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn.ensemble as ensemble # ensemble learning: 集成学习"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=4, estimator=RandomForestClassifier(),\n",
       "             param_grid={'criterion': ['entropy', 'gini'],\n",
       "                         'max_depth': [5, 6, 7, 8],\n",
       "                         'max_features': [0.3, 0.4, 0.5],\n",
       "                         'min_samples_split': [4, 8, 12, 16],\n",
       "                         'n_estimators': [11, 13, 15]},\n",
       "             scoring='roc_auc')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2. 随机森林算法\n",
    "\n",
    "rfc = ensemble.RandomForestClassifier()\n",
    "\n",
    "rfc_cv = GridSearchCV(estimator=rfc, param_grid=param_grid,\n",
    "                      scoring='roc_auc', cv=4)\n",
    "\n",
    "rfc_cv.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "随机森林精确度...\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.89      0.93       394\n",
      "           1       0.48      0.79      0.59        52\n",
      "\n",
      "    accuracy                           0.87       446\n",
      "   macro avg       0.72      0.84      0.76       446\n",
      "weighted avg       0.91      0.87      0.89       446\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 3. 使用随机森林对测试集进行预测\n",
    "\n",
    "predict_test = rfc_cv.predict(X_test)\n",
    "\n",
    "print('随机森林精确度...')\n",
    "print(metrics.classification_report(predict_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "随机森林 AUC...\n",
      "AUC = 0.8371\n"
     ]
    }
   ],
   "source": [
    "# 4. AUC的值\n",
    "\n",
    "print('随机森林 AUC...')\n",
    "fpr_test, tpr_test, th_test = metrics.roc_curve(predict_test, y_test) # 构造 roc 曲线\n",
    "\n",
    "print('AUC = %.4f' %metrics.auc(fpr_test, tpr_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'criterion': 'gini',\n",
       " 'max_depth': 8,\n",
       " 'max_features': 0.5,\n",
       " 'min_samples_split': 4,\n",
       " 'n_estimators': 13}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 5. 查看最佳参数（有可能不是最优参数，仍然需要调参）\n",
    "\n",
    "rfc_cv.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "随机森林精确度...\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.88      0.94       410\n",
      "           1       0.42      1.00      0.59        36\n",
      "\n",
      "    accuracy                           0.89       446\n",
      "   macro avg       0.71      0.94      0.76       446\n",
      "weighted avg       0.95      0.89      0.91       446\n",
      "\n",
      "随机森林 AUC...\n",
      "AUC = 0.9390\n"
     ]
    }
   ],
   "source": [
    "# 6. 调整决策边界，调参\n",
    "\n",
    "param_grid = {\n",
    "    'criterion':['entropy','gini'],\n",
    "    'max_depth':[7, 8, 10, 12], # 前面的 5，6 也可以适当的去掉，反正已经没有用了\n",
    "    'n_estimators':[11, 13, 15, 17, 19],  # 决策树个数 - 随机森林特有参数\n",
    "    'max_features':[0.4, 0.5, 0.6, 0.7], # 每棵决策树使用的变量占比 - 随机森林特有参数\n",
    "    'min_samples_split':[2, 3, 4, 8, 12, 16]  # 叶子的最小拆分样本量\n",
    "}\n",
    "\n",
    "# 7. 重复上述步骤，再次训练，寻找最优参数\n",
    "\n",
    "rfc_cv = GridSearchCV(estimator=rfc, param_grid=param_grid,\n",
    "                      scoring='roc_auc', cv=4)\n",
    "\n",
    "rfc_cv.fit(X_train, y_train)\n",
    "\n",
    "# 8. 使用随机森林对测试集进行预测\n",
    "predict_test = rfc_cv.predict(X_test)\n",
    "\n",
    "print('随机森林精确度...')\n",
    "print(metrics.classification_report(predict_test, y_test))\n",
    "\n",
    "print('随机森林 AUC...')\n",
    "fpr_test, tpr_test, th_test = metrics.roc_curve(predict_test, y_test) # 构造 roc 曲线\n",
    "\n",
    "print('AUC = %.4f' %metrics.auc(fpr_test, tpr_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'criterion': 'entropy',\n",
       " 'max_depth': 10,\n",
       " 'max_features': 0.4,\n",
       " 'min_samples_split': 4,\n",
       " 'n_estimators': 13}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 8. 查看最优参数\n",
    "\n",
    "rfc_cv.best_params_ "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 树模型参数:\n",
    "\n",
    "-  1.criterion  gini  or  entropy\n",
    "\n",
    "-  2.splitter  best or random 前者是在所有特征中找最好的切分点 后者是在部分特征中（数据量大的时候）\n",
    "\n",
    "-  3.max_features  None（所有），log2，sqrt，N  特征小于50的时候一般使用所有的\n",
    "\n",
    "-  4.max_depth  数据少或者特征少的时候可以不管这个值，如果模型样本量多，特征也多的情况下，可以尝试限制下\n",
    "\n",
    "-  5.min_samples_split  如果某节点的样本数少于min_samples_split，则不会继续再尝试选择最优特征来进行划分如果样本量不大，不需要管这个值。如果样本量数量级非常大，则推荐增大这个值。\n",
    "\n",
    "-  6.min_samples_leaf  这个值限制了叶子节点最少的样本数，如果某叶子节点数目小于样本数，则会和兄弟节点一起被剪枝，如果样本量不大，不需要管这个值，大些如10W可是尝试下5\n",
    "\n",
    "-  7.min_weight_fraction_leaf 这个值限制了叶子节点所有样本权重和的最小值，如果小于这个值，则会和兄弟节点一起被剪枝默认是0，就是不考虑权重问题。一般来说，如果我们有较多样本有缺失值，或者分类树样本的分布类别偏差很大，就会引入样本权重，这时我们就要注意这个值了。\n",
    "\n",
    "-  8.max_leaf_nodes 通过限制最大叶子节点数，可以防止过拟合，默认是\"None”，即不限制最大的叶子节点数。如果加了限制，算法会建立在最大叶子节点数内最优的决策树。如果特征不多，可以不考虑这个值，但是如果特征分成多的话，可以加以限制具体的值可以通过交叉验证得到。\n",
    "\n",
    "-  9.class_weight 指定样本各类别的的权重，主要是为了防止训练集某些类别的样本过多导致训练的决策树过于偏向这些类别。这里可以自己指定各个样本的权重如果使用“balanced”，则算法会自己计算权重，样本量少的类别所对应的样本权重会高。\n",
    "\n",
    "- 10.min_impurity_split 这个值限制了决策树的增长，如果某节点的不纯度(基尼系数，信息增益，均方差，绝对差)小于这个阈值则该节点不再生成子节点。即为叶子节点 。\n",
    "- n_estimators:要建立树的个数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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